As service providers deploy larger and larger access networks, scalability problems can arise, which present various challenges from an operational standpoint. For example, edge reachability problems can occur when deployments scale beyond the number of users serviceable by a network edge. To address these issues, load balancing nodes have been introduced as a first logical hop for receiving and/or aggregating signaling traffic that enters a service provider's network.
Problems still exist, however, in determining the next-hop entity within the network after the traffic is received at the load balancing node. Currently, the load balancing node will assign new session requests to a next-hop entity according to a round robin algorithm. In this traffic distribution scheme, traffic from a new client is anchored to the next-hop entity having the lowest central processing unit (CPU) utilization rate as reported at a predetermined time interval.
This (i.e., round robin) traffic distribution scheme is problematic, as it assumes that all next-hop entities are homogeneous, meaning that each next-hop entity will perform and scale at a same level. As next-hop entitles may differ in regards to processing capabilities, round robin traffic distribution schemes can overload network entities that are slower and/or have lower processing capabilities.
The round robin traffic distribution scheme also assumes that the CPU utilization rate being reported by each next-hop entity reflects a true (real-time) indication of how “busy” the entity is. However, in some aspects, “stale” data is relied on, which may result in rejected requests and/or dropped packets. Stale data relates to the latency associated with an entity in sending periodic utilization information to the load balancer, which causes the load balancer to make incorrect assumptions based on the latent information in times of avalanche or high session establishment and before the next metric is received.
Accordingly, a need exists for methods, systems, and computer readable media for improved traffic distribution via implementing load balancer traffic policies.